The split operation allows one to obtain two distinct random number
generators. This is very useful in functional programs (for example, when
passing a random number generator down to recursive calls), but very
little work has been done on statistically robust implementations of
split ([Random, Random]
are the only examples we know of).

The second condition ensures that genRange cannot examine its
argument, and hence the value it returns can be determined only by the
instance of RandomGen. That in turn allows an implementation to make
a single call to genRange to establish a generator's range, without
being concerned that the generator returned by (say) next might have
a different range to the generator passed to next.

Standard random number generators

The result of repeatedly using next should be at least as statistically
robust as the Minimal Standard Random Number Generator described by
[Random, Random].
Until more is known about implementations of split, all we require is
that split deliver generators that are (a) not identical and
(b) independently robust in the sense just given.

The Show and Read instances of StdGen provide a primitive way to save the
state of a random number generator.
It is required that read (show g) == g.

In addition, reads may be used to map an arbitrary string (not necessarily one
produced by show) onto a value of type StdGen. In general, the Read
instance of StdGen has the following properties:

The function mkStdGen provides an alternative way of producing an initial
generator, by mapping an Int into a generator. Again, distinct arguments
should be likely to produce distinct generators.

The global random number generator

There is a single, implicit, global random number generator of type
StdGen, held in some global variable maintained by the IO monad. It is
initialised automatically in some system-dependent fashion, for example, by
using the time of day, or Linux's kernel random number generator. To get
deterministic behaviour, use setStdGen.

Uses the supplied function to get a value from the current global
random generator, and updates the global generator with the new generator
returned by the function. For example, rollDice gets a random integer
between 1 and 6:

Takes a range (lo,hi) and a random number generator
g, and returns a random value uniformly distributed in the closed
interval [lo,hi], together with a new generator. It is unspecified
what happens if lo>hi. For continuous types there is no requirement
that the values lo and hi are ever produced, but they may be,
depending on the implementation and the interval.